Efficient Multitarget Particle Filters for Ground Target Tracking and Classification

Many factors make the ground target tracking problem decidedly nonlinear and non-Gaussian. Some of these factors include the relatively poor angular accuracy of GMTI sensors, the presence of persistent clutter and target obscuration, and the complexity of target maneuvers. Because these difficulties can lead to a multimodal posterior density, a Bayesian filtering solution is more appropriate than a point estimate. Recently, the particle filter has emerged as a Bayesian inference technique that is both powerful and simple to implement. In Phase I, we established both the feasibility and necessity of using multiple-target particle filters when two or more tracks are linked through measurement contention. We also developed an efficient way to implement these filters by adaptively managing the type of particle filters, the number of particles, and the enumeration of hypotheses during data association. In Phase II, we propose to transition our Phase I prototype into a fully functional particle filter-based ground target tracker/classifier with commercial potential. The most challenging aspects of this transition are (i) the inclusion of feature data to perform joint tracking/classification, (ii) the development of a particle filter-based track initiation method, and (iii) the design of an efficient importance sampling scheme.